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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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2
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Shimada K. Correlations among Firing Rates of Tactile, Thermal, Gustatory, Olfactory, and Auditory Sensations Mimicked by Artificial Hybrid Fluid (HF) Rubber Mechanoreceptors. SENSORS (BASEL, SWITZERLAND) 2023; 23:4593. [PMID: 37430506 DOI: 10.3390/s23104593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 07/12/2023]
Abstract
In order to advance the development of sensors fabricated with monofunctional sensation systems capable of a versatile response to tactile, thermal, gustatory, olfactory, and auditory sensations, mechanoreceptors fabricated as a single platform with an electric circuit require investigation. In addition, it is essential to resolve the complicated structure of the sensor. In order to realize the single platform, our proposed hybrid fluid (HF) rubber mechanoreceptors of free nerve endings, Merkel cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles mimicking the bio-inspired five senses are useful enough to facilitate the fabrication process for the resolution of the complicated structure. This study used electrochemical impedance spectroscopy (EIS) to elucidate the intrinsic structure of the single platform and the physical mechanisms of the firing rate such as slow adaption (SA) and fast adaption (FA), which were induced from the structure and involved the capacitance, inductance, reactance, etc. of the HF rubber mechanoreceptors. In addition, the relations among the firing rates of the various sensations were clarified. The adaption of the firing rate in the thermal sensation is the opposite of that in the tactile sensation. The firing rates in the gustation, olfaction, and auditory sensations at frequencies of less than 1 kHz have the same adaption as in the tactile sensation. The present findings are useful not only in the field of neurophysiology, to research the biochemical reactions of neurons and brain perceptions of stimuli, but also in the field of sensors, to advance salient developments in sensors mimicking bio-inspired sensations.
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Affiliation(s)
- Kunio Shimada
- Faculty of Symbiotic Systems Sciences, Fukushima University, 1 Kanayagawa, Fukushima 960-1296, Japan
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Shimada K. Estimation of Fast and Slow Adaptions in the Tactile Sensation of Mechanoreceptors Mimicked by Hybrid Fluid (HF) Rubber with Equivalent Electric Circuits and Properties. SENSORS (BASEL, SWITZERLAND) 2023; 23:1327. [PMID: 36772367 PMCID: PMC9920702 DOI: 10.3390/s23031327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/20/2023] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
In order to advance engineering applications of robotics such as wearable health-monitoring devices, humanoid robots, etc., it is essential to investigate the tactile sensations of artificial haptic sensors mimicking bioinspired human cutaneous mechanoreceptors such as free nerve endings, Merkel's cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles. The generated receptor's potential response to extraneous stimuli, categorized as slow adaption (SA) or fast adaption (FA), is particularly significant as a typical property. The present study addressed the estimation of SA and FA by utilizing morphologically fabricated mechanoreceptors made of our proposed magnetically responsive intelligent fluid, hybrid fluid (HF), and by applying our proposed electrolytic polymerization. Electric circuit models of the mechanoreceptors were generated using experimental data on capacitance and inductance on the basis of the electric characteristics of impedance. The present results regarding equivalent firing rates based on FA and SA are consistent with the FA and SA findings of vital mechanoreceptors by biomedical analysis. The present investigative process is useful to clarify the time of response to a force on the fabricated artificial mechanoreceptor.
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Affiliation(s)
- Kunio Shimada
- Faculty of Symbiotic Systems Sciences, Fukushima University, 1 Kanayagawa, Fukushima 960-1296, Japan
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Migalev AS, Vigasina KD, Gotovtsev PM. A review of motor neural system robotic modeling approaches and instruments. BIOLOGICAL CYBERNETICS 2022; 116:271-306. [PMID: 35041073 DOI: 10.1007/s00422-021-00918-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
In this review, we are considering an actively developing tool in neuroscience-robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.
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Affiliation(s)
- Alexander S Migalev
- National Research Center "Kurchatov Intitute", 1, Akademika Kurchatova pl., Moscow, 123182, Russia
| | - Kristina D Vigasina
- Institute of Higher Nervous Activity and Neurophysiology of RAS, 5A, Butlerova st., Moscow, 117485, Russia
| | - Pavel M Gotovtsev
- National Research Center "Kurchatov Intitute", 1, Akademika Kurchatova pl., Moscow, 123182, Russia
- Moscow Institute of Physics and Technology 9, Institutsky per., Dolgoprudny, Moscow Region, 141701, Russian Federation
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Han J, Tcho I, Jeon S, Yu J, Kim W, Choi Y. Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105076. [PMID: 35032113 PMCID: PMC8948587 DOI: 10.1002/advs.202105076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/01/2021] [Indexed: 05/19/2023]
Abstract
A self-powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self-powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low-pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software-based simulation. Furthermore, a fully hardware-based breath-monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.
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Affiliation(s)
- Joon‐Kyu Han
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Il‐Woong Tcho
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seung‐Bae Jeon
- Electronics Engineering DepartmentHanbat National University125 Dongseo‐daero, Yuseong‐guDaejeon34158Republic of Korea
| | - Ji‐Man Yu
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Weon‐Guk Kim
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Yang‐Kyu Choi
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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Li F, Wang R, Song C, Zhao M, Ren H, Wang S, Liang K, Li D, Ma X, Zhu B, Wang H, Hao Y. A Skin-Inspired Artificial Mechanoreceptor for Tactile Enhancement and Integration. ACS NANO 2021; 15:16422-16431. [PMID: 34597014 DOI: 10.1021/acsnano.1c05836] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Mechanoreceptors endow humans with the sense of touch by translating the external stimuli into coded spikes, inspiring the rise of artificial mechanoreceptor systems. However, to incorporate slow adaptive receptors-like pressure sensors with artificial neurons remains a challenge. Here we demonstrate an artificial mechanoreceptor by rationally integrating a polypyrrole-based resistive pressure sensor with a volatile NbOx memristor, to mimic the tactile sensation and perception in natural skin, respectively. The artificial mechanoreceptor enables the tactile sensory coding by converting the external mechanical stimuli into strength-modulated electrical spikes. Also, tactile sensation enhancement is achieved by processing the spike frequency characteristics with the pulse coupled neural network. Furthermore, the artificial mechanoreceptor can integrate signals from parallel sensor channels and encode them into unified electrical spikes, resembling the coding of intensity in tactile neural processing. These results provide simple and efficient strategies for constructing future bio-inspired electronic systems.
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Affiliation(s)
- Fanfan Li
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710071, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710071, China
| | - Chunyan Song
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Momo Zhao
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710071, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Zhejiang University, Hangzhou 310027, China
| | - Saisai Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710071, China
| | - Kun Liang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Zhejiang University, Hangzhou 310027, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Zhejiang University, Hangzhou 310027, China
| | - Xiaohua Ma
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710071, China
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Yue Hao
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
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Rahiminejad E, Parvizi-Fard A, Iskarous MM, Thakor NV, Amiri M. A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2333-2344. [PMID: 34673491 DOI: 10.1109/tnsre.2021.3120446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume [Formula: see text] of power. The occupied silicon area is [Formula: see text] for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies.
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Parvizi-Fard A, Salimi-Nezhad N, Amiri M, Falotico E, Laschi C. Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors. Sci Rep 2021; 11:2109. [PMID: 33483529 PMCID: PMC7822817 DOI: 10.1038/s41598-021-81199-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors. Indeed, the functional concept of the nociceptor is implemented on the FPGA for the first time. One of the main features of biological tactile afferents is that their distal axon branches in the skin, creating complex receptive fields. Given these physiological observations, the bio-inspired afferents are randomly connected to the several neighboring mechanoreceptors with different weights to form their own receptive field. To test the performance of the proposed neuromorphic chip in sharpness detection, a robotic system with three-degree of freedom equipped with the tactile sensor indents the 3D-printed objects. Spike responses of the biomimetic afferents are then collected for analysis by rate and temporal coding algorithms. In this way, the impact of the innervation mechanism and collaboration of afferents and nociceptors on sharpness recognition are investigated. Our findings suggest that the synergy between sensory afferents and nociceptors conveys more information about tactile stimuli which in turn leads to the robustness of the proposed neuromorphic system against damage to the taxels or afferents. Moreover, it is illustrated that spiking activity of the biomimetic nociceptors is amplified as the sharpness increases which can be considered as a feedback mechanism for prosthesis protection. This neuromorphic approach advances the development of prosthesis to include the sensory feedback and to distinguish innocuous (non-painful) and noxious (painful) stimuli.
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Affiliation(s)
- Adel Parvizi-Fard
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Nima Salimi-Nezhad
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- grid.412112.50000 0001 2012 5829Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Parastar Ave., Kermanshah, Iran
| | - Egidio Falotico
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Cecilia Laschi
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy ,grid.4280.e0000 0001 2180 6431Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore
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Parvizi-Fard A, Amiri M, Kumar D, Iskarous MM, Thakor NV. A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Sci Rep 2021; 11:1320. [PMID: 33446742 PMCID: PMC7809061 DOI: 10.1038/s41598-020-80132-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/17/2020] [Indexed: 01/24/2023] Open
Abstract
To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications.
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Affiliation(s)
- Adel Parvizi-Fard
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
| | - Deepesh Kumar
- SINAPSE Laboratory, National University of Singapore, Singapore, Singapore
| | - Mark M Iskarous
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish V Thakor
- SINAPSE Laboratory, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
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Yavari F, Amiri M, Rahatabad FN, Falotico E, Laschi C. Spike train analysis in a digital neuromorphic system of cutaneous mechanoreceptor. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Salimi-Nezhad N, Ilbeigi E, Amiri M, Falotico E, Laschi C. A Digital Hardware System for Spiking Network of Tactile Afferents. Front Neurosci 2020; 13:1330. [PMID: 32009869 PMCID: PMC6971225 DOI: 10.3389/fnins.2019.01330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 11/26/2019] [Indexed: 11/13/2022] Open
Abstract
In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA). The four different digital circuits are then compared from source utilization point of view to find the minimum cost circuit for creating a population of digital afferents. In this way, the firing responses of both SA-I and FA-I afferents are physically measured in hardware. Finally, a population of 243 afferents consisting of 90 SA-I and 153 FA-I digital neuromorphic circuits are implemented on the FPGA. The FPGA also receives nine inputs from the force sensors through an interfacing board. Therefore, the data of multiple inputs are processed by the spiking network of tactile afferents, simultaneously. Benefiting from parallel processing capabilities of FPGA, the proposed architecture offers a low-cost neuromorphic structure for tactile information processing. Applying machine learning algorithms on the artificial spiking patterns collected from FPGA, we successfully classified three different objects based on the firing rate paradigm. Consequently, the proposed neuromorphic system provides the opportunity for development of new tactile processing component for robotic and prosthetic applications.
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Affiliation(s)
- Nima Salimi-Nezhad
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Erfan Ilbeigi
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
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Predict Afferent Tactile Neural Signal for Artificial Nerve Based on Finite Element Human Hand Model. INTELLIGENT ROBOTICS AND APPLICATIONS 2019. [DOI: 10.1007/978-3-030-27526-6_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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